数据集不均衡下的设备故障程度识别方法研究

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (20) : 178-182.

论文

段礼祥1 ，郭晗1, 2 ，王金江1

作者信息 +

Mechanical fault Severity Identification Methods under Unbalanced Datasets

DUAN Li-xiang1, GUO Han1, 2, WANG Jin-jiang1

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文章历史 +

摘要

在机械故障诊断中，由于故障样本难以收集且数量远远少于正常样本，即产生数据集不均衡问题。这将导致传统的分类算法如支持向量机（SVM）在处理不均衡分类问题时对少数类样本（故障样本）的分类准确率过低。加权支持向量机(C-SVM)算法是一种处理样本集不均衡问题的常用算法，可以在一定程度上改善少数类样本的分类准确率。但是在故障程度相近时会导致样本间距过小，加权C-SVM算法对这类故障样本的识别精度不理想。为提高数据集不均衡下故障程度相近样本的分类准确率，采用二叉树结构与加权C-SVM相结合的方法，综合考虑样本类间距离、类内距离和不均衡程度，优化二叉树结构。结果表明，该算法能够有效处理样本距离过近的不均衡数据集分类问题，从而提高了故障程度相近样本的分类准确率。

关键词：故障诊断；故障程度识别；数据集不均衡；二叉树加权支持向量机

Abstract

In mechanical fault diagnosis, the samples under fault condition are often difficult to obtain, also called unbalanced dataset issue, which will lead to very low classification accuracy with the conventional algorithm, such as support vector machine (SVM). Weighted C-support vector machine shows the improved performance, however, due to the small sample space caused by close fault severities, the classification accuracy of weighted C-support vector machine is still not up to satisfactory. To improve the classification accuracy for close fault severity cases under unbalanced dataset, this paper presents an approach integrating weighted c-support vector machine algorithm with binary tree structure, named as BT-CSVM. The binary structure is then optimized taking account of sample space of class-to-class, sample space of inter-class, and unbalance degree. Experimental results show that the proposed method can effectively deal with unbalanced dataset problem by greatly improving the classification accuracy for close fault severity cases.

关键词

故障诊断

/ 故障程度识别 / 数据集不均衡 / 二叉树加权支持向量机

Key words

fault diagnosis / fault severity identification / unbalanced data / binary tree weighted C-support SVM

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段礼祥1,郭晗1, 2,王金江1. 数据集不均衡下的设备故障程度识别方法研究[J]. 振动与冲击, 2016, 35(20): 178-182

DUAN Li-xiang1, GUO Han1, 2, WANG Jin-jiang1. Mechanical fault Severity Identification Methods under Unbalanced Datasets[J]. Journal of Vibration and Shock, 2016, 35(20): 178-182

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